10 Awesome Machine Learning Applications of Today

10 Awesome Machine Learning Applications of Today


“Machine Learning – The Hot Technology Nurturing the Growth of Cool Products”

If you keep yourself updated about technology news, you are probably seeing mentions about machine learning everywhere- from voice assistants to self-driving cars, and for good reasons. Everyday a new app, product, or service unveils that it is using machine learning to get smarter and better.

You’ve likely used machine learning on your way to work (Google Maps for suggesting Traffic Route, making an online purchase (on Amazon or Walmart), and for communicating with your friends online (Facebook). Not to mention, in the process of navigating to this blog page on your screen through Google Search, you almost certainly used Machine Learning. The applications of machine learning are everywhere - email spam filter, product recommendations, chatbots, image recognition, etc. This post will try to give novice readers plenty of real-world machine learning applications where the ML technology works like a charm.

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If you are not familiar with Machine Learning, you can read our earlier blog on - What is Machine Learning?

Machine Learning Applications Across Various Industries

 Applications of Machine Learning

This blog post covers the most common and coolest machine learning business applications across various domains-

  • Machine Learning Applications in Healthcare

  • Machine Learning Applications in Finance

  • Machine Learning Applications in Retail

  • Machine Learning Applications in Travel

  • Machine Learning Applications in Media

Machine Learning Applications in Healthcare

Machine Learning Applications in Healthcare

Doctors and medical practitioners will soon be able to predict with accuracy on how long patients with fatal diseases will live. Medical systems will learn from data and help patients save money by skipping unnecessary tests. Radiologists will be replaced by machine learning algorithms. McKinsey Global Institute estimates that applying machine learning techniques to better inform decision making could generate up to $100 billion in value based on optimized innovation, enhanced efficiency of clinical trials, and the creation of various novel tools for physicians, insurers and consumers. Computers and Robots cannot replace doctors or nurses, however, the use of life-saving technology (machine learning) can definitely transform the healthcare domain. When we talk about the efficiency of machine learning, more data produces effective results – and the healthcare industry is residing on a data goldmine.

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i) Drug Discovery/Manufacturing

Manufacturing or discovering a new drug is expensive and lengthy process as thousands of compounds need to be subjected to a series of tests, and only a single one might result in a usable drug. Machine learning can speed up one or more of these steps in this lengthy multi-step process.

Machine Learning Examples in Healthcare for Drug Discovery

  • Pfizer is using IBM Watson on its immuno-oncology (a technique that uses body’s immune system to help fight cancer) research. This is one of the most significant uses of IBM Watson for drug discovery. Pfizer has been using machine learning for years to sieve through the data to facilitate research in the areas of drug discovery (particularly the combination of multiple drugs) and determine the best participant for a clinical trial.

Here’s a short clip on how Pfizer will utilize IBM Watson Health for Immuno-Oncology Research -

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ii) Personalized Treatment/Medication

Imagine when you walk in to visit your doctor with some kind of an ache in your stomach. After snooping into your symptoms, the doctor inputs them into the computer that extracts the latest research that the doctor might need to know about how to treat your ache. You have an MRI and a computer helps the radiologist detect problems that possibly could be too small for the human eye to see. In the end, a computer scans all your health records and family medical history and compares it to the latest research to advice a treatment protocol that is particularly tailored to your problem. Machine learning is all set to make a mark in personalized care.

Personalized treatment has great potential for growth in future, and machine learning could play a vital role in finding what kind of genetic makers and genes respond to a particular treatment or medication. Personalized medication or treatment based on individual health records paired with analytics is a hot research area as it provides better disease assessment. In future, increased usage of sensor integrated devices and mobile apps with sophisticated remote monitoring and health-measurement capabilities, there would be another data deluge that could be used for treatment efficacy. Personalized treatment facilitates health optimization and also reduces overall healthcare costs.

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Machine Learning Examples in Healthcare for Personalized Treatment

  • A major problem that drug manufacturers often have is that a potential drug sometimes work only on a small group in clinical trial or it could be considered unsafe because a small percentage of people developed serious side effects. Genentech, a member of the Roche Group collaborated with GNS Healthcare to innovate solutions and treatments using biomedical data. Genentech will make use of GNS Reverse Engineering and Forward Simulation to look for patient response markers based on genes which could lead to providing targeted therapies for patients.

Dr. Sara Kenkare-Mitra, Señor VP, Development Science at Genentech talks about science, drug research, personalized medicine -

Machine Learning Applications in Finance

Applications of Machine Learning in Banking and Finance

More than 90% of the top 50 financial institutions around the world are using machine learning and advanced analytics. The application of machine learning in Finance domain helps banks offer personalized services to customers at lower cost, better compliance and generate greater revenue.

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Machine Learning Examples in Finance for Fraud Detection

Thanks to the e-commerce boom, most of us now purchase products online. With increasing online shopping, fraudulent transactions are also on the rise. To combat this, companies are using machine learning algorithms to identify and block fraudsters. You are watching “Game of Thrones” when you get a call from your bank asking if you have swiped your card for “$X” at a store in your city to buy a gadget. It was not you who bought the expensive gadget using your card – in fact, it has been in your pocket all noon. How did the bank flag this purchase as fraudulent? All thanks to Machine Learning! Financial fraud costs $80 billion annually, of which, Americans alone are exposed to a risk worth $50 billion per annum.

One of the core machine learning use cases in the banking/finance domain is to combat fraud. Machine learning is best suited for this use case as it can scan through huge amounts of transactional data and identify if there is any unusual behavior. Every transaction a customer makes is analyzed in real-time and given a fraud-score that represents the likelihood of the transaction being fraudulent. In case of a fraud transaction, the transaction is blocked or handed over for a manual review depending on the severity of fraud-like patterns. All of this happens in the blink of an eye. If the fraud score is above a particular threshold, a rejection will be triggered automatically which would otherwise be difficult without the application of machine learning techniques as humans cannot reviews 1000’s data points in seconds and make a decision.

  • Citibank has collaborated with Portugal-based fraud detection company Feedzai that works in real-time to identify and eliminate fraud in online and in-person banking by alerting the customer.
  • PayPal is using machine learning to fight money laundering. PayPal has several machine learning tools that compare billions of transactions and can accurately differentiate between what is a legitimate and fraudulent transaction amongst the buyers and sellers.

Neil Jacobstein explores how machine learning and data analytics are revolutionizing credit, risk, and fraud to address the world's biggest challenges -

Machine Learning Examples in Finance for Focused Account Holder Targeting

Wondering how banks know about their most valuable account holders? – The secret to this is the underlying machine learning algorithms which confirm that best customers are those with large balances and loans.

Wells Fargo utilized machine learning to identify that a group of home maker moms in Florida with huge social media presence were their most influential and preferred banking customers in terms of referrals. The machine learning algorithm identified patterns that the humans have missed earlier which helped Wells-Fargo target those key customers.

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What the future holds for AI and machine learning in banking and finance?

We can expect a robot to give a sound investing advice as companies like Betterment and Wealthfront make attempts to automate the best practices of investors and provide them to customers at nominal costs than traditional fund managers.

Machine Learning Applications in Retail

Machine learning in retail is more than just a latest trend, retailers are implementing big data technologies like Hadoop and Spark to build big data solutions and quickly realizing the fact that it’s only the start. They need a solution which can analyse the data in real-time and provide valuable insights that can translate into tangible outcomes like repeat purchasing. Machine learning algorithms process this data intelligently and automate the analysis to make this supercilious goal possible for retail giants like Amazon, Target, Alibaba and Walmart.

Machine Learning Application in Retail

Machine Learning Examples in Retail for Product Recommendations

According to The Realities of Online Personalisation Report, 42% of retailers are using personalized product recommendations using machine learning technology. It is no secret that customers always look for personalized shopping experiences, and these recommendations increase the conversion rates for the retailers resulting in fantastic revenue.

  • The moment you start browsing for items on Amazon, you see recommendations for products you are interested in as “Customers Who Bought this Product Also Bought” and “Customers who viewed this product also viewed”, as well specific tailored product recommendation on the home page, and through email. Amazon uses Artificial Neural Networks machine learning algorithm to generate these recommendations for you.
  • To make smart personalized recommendations, Alibaba has developed “E-commerce Brain” that makes use of real-time online data to build machine learning models for predicting what customers want and recommending the relevant products based on their recent order history, bookmarking, commenting, browsing history,  and other actions.
  • Media companies like Netflix and Youtube also employ recommendation systems to suggest to users their favorite shows and videos.  Next time you receive a ‘people also liked’ or ‘just for you’ recommendation from Amazon, Netflix, or any other site you know who to give credit to. 

Watch this Video Clip to Understand the Amazon Algorithm -

Machine Learning Examples in Retail for Improved Customer Service

According to a story published on Harvard Business Review, finding new customers is 5 to 25 times expensive than retaining old customers. Customer Loyalty is a commodity that cannot be bought and retailers are tapping into machine learning technology to make the overall shopping experience happy and satisfactory so that they do not move on from one retailer to another.

When you have trouble with a purchased product, trying to get help can often be a frustrating experience. Customers often complain about exceedingly long waiting times on phone calls , having to explain the problem every time to a new customer service executive every time they call up, or unqualified advice from the support representatives. Machine learning will help automate this process through chatbots and robots that will answer the phone calls. There are two main types of chatbots -

  • Task-oriented chatbots - These are single-purpose chatbots that perform scripted functions. For example, answering common FAQs, customer service, etc.
  • Conversational chatbots - These chatbots are popularly called virtual assistants. Such chatbots are contextually ‘aware’ and improve over time as they leverage NLP, ML, and NLU (Natural language understanding) to learn from user inputs. Apple’s Siri, Amazon’s Alexa, and Google’s virtual assistant are examples of conversational chatbots.

 Macy’s StoreHelp is a simple chatbot that helps customers locate the products within the store and also answers simple questions that customers might have pertaining to a particular product.

Retailers mine customer actions, transactions, and social data to identify customers who are at a high risk of switching to a competitor. This information is then combined with profitability data so that they can optimize their next best action strategies and personalize an end-to-end shopping experience for the customer.

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Machine Learning Applications in Travel

By 2030, there will be a solution for each unique travel purpose. Instead of commuting to work and stressing about finding parking, you can take a ride sharing service. For leisurely trips, self-driving cars will be able to handle transportation, while your relax and watch a movie. —said ALVIN CHIN, BMW TECHNOLOGY CORPORATION

Machine Learning Applications in Travel

Machine Learning Examples in Travel for Dynamic Pricing

How does Uber determine the price of your ride?

How does Uber enable ridesharing by optimally matching you other passengers to minimize roundabout routes?

How does Uber minimize the wait time once you book a car?

The answer to all these questions is Machine Learning.

One of Uber’s biggest uses of machine learning comes in the form of surge pricing, a machine learning model nicknamed as “Geosurge” at Uber. If you are getting late for a meeting and you need to book an Uber in crowded area, get ready to pay twice the normal fare. In 2011, during New Year’s Eve in New York, Uber charged $37 to $135 for one mile journey. Uber leverages predictive modelling in real-time based on traffic patterns, supply and demand. Uber has acquired a patent on surge pricing. However, customer backlash on surge-pricing is strong, so Uber is using machine learning to predict where demand will be high so that drivers can prepare in advance to meet the demand, and surge pricing can be reduced to a greater extent.

Machine Learning Examples in Travel for Sentiment Analysis

Applications of sentiment analysis include campaign monitoring, brand monitoring, stock market analysis, compliance monitoring, etc. Let’s understand sentiment analysis with the simplest implementation - using a word list with scores ranging from +5 (positive) to -5 (negative). For this, let’s use the AFINN scored word list. Say one of your customers wrote - I loved the product, but the packaging was not good. In the AFINN word list, ‘loved’ and ‘not good’ have +3 and -2 scores, respectively. If you combine these two scores, you will get +1. This means the user sentiment was mildly positive. Again this is the simplest example of sentiment analysis. Complex models combine NLP and machine learning algorithms to analyze large pieces of text.  According to Amadeus IT group, 90% of American travellers with a smartphone share their photos and travel experience on social media and review services. TripAdvisor gets about 280 reviews from travellers every minute. With a large pool of valuable data from 390 million unique visitors and 435 million reviews, TripAdvisor analyses this information to enhance its service. Machine learning techniques at TripAdvisor focus on analysing brand-related reviews.

Machine Learning Applications in Social Media

Machine Learning Applications in Social Media

Machine learning offers the most efficient means of engaging billions of social media users. From personalizing news feed to rendering targeted ads, machine learning is the heart of all social media platforms for their own and user benefits. Social media and chat applications have advanced to a great extent that users do not pick up the phone or use email to communicate with brands – they leave a comment on Facebook or Instagram expecting a speedy reply than the traditional channels.

How Facebook uses Machine Learning ?

Facebook’s auto-tagging feature is the most popular application of machine learning that employs image recognition. Facebook identifies your friend’s face with only a few tagged pictures as its face recognition algorithm has 98% accuracy (comparable with human recognition abilities). Moreover, image recognition technology is used in several other fields ranging from self-driving cars to policing. Image recognition involves three major steps - a) Detection, b) Classification and c) Recognition. Take our face recognition example. To unlock your smartphone, the system has to detect a face, then classify it as a human face, and lastly, recognize your face. To achieve image recognition ability, the neural network (algorithms designed to recognize patterns) is fed with a vast number of labeled images, and then the machine learning model is trained to differentiate between different objects.

Some machine learning examples that you must be using and loving in your social media accounts without knowing the fact that their interesting features are machine learning applications -

  • Earlier Facebook used to prompt users to tag their friends but nowadays the social networks artificial neural networks machine learning algorithm identifies familiar faces from the contact list. The ANN algorithm mimics the structure of the human brain to power facial recognition.
  • The professional network LinkedIn knows where you should apply for your next job, whom you should connect with, and how your skills stack up against your peers as you search for a new job.

If you have reached till here, you would know modern-day applications cannot be imagined without machine learning algorithms, or perhaps the future cannot be imagined without ML. From simple chatbots to self-driving cars, every technology has a machine learning model. It is only a matter of time to see  practical applications of machine learning unlock more technology advancements.

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